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Optimising Decarbonisation Investment for Firms towards Environmental Sustainability

Tran, Trung-Hieu; Mao, Yong; Siebers, Peer-Olaf

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Authors

Trung-Hieu Tran

YONG MAO yong.mao@nottingham.ac.uk
Associate Professor



Abstract

We develop a mixed-integer non-linear programming model for firms’ decarbonisation investment decision-making towards a sustainable environment. Our model seeks the optimal investment for a firm to achieve maximum profit under constraints derived from its environmental protection awareness and the government’s taxation policy. We use an uncertainty theory to formulate the relationship of a firm’s environmental protection awareness and its investment budget levels. Governments’ taxation policy is modelled by a step-wise linear function, where reduced carbon dioxide emission can help the firm reduce taxation. A linearisation is proposed to solve the non-linear problem efficiently. A case study for a sector of electronic component manufacturers in Nottingham, the United Kingdom, demonstrates the practical implementation of the proposed model. Several large-sized instances, which were randomly generated, were utilised to evaluate the the efficiency of model in terms of computational time. Our model can be used to explore budget options to obtain higher profits under a particular taxation policy.

Citation

Tran, T., Mao, Y., & Siebers, P. (2019). Optimising Decarbonisation Investment for Firms towards Environmental Sustainability. Sustainability, 11(20), https://doi.org/10.3390/su11205718

Journal Article Type Article
Acceptance Date Oct 11, 2019
Online Publication Date Oct 16, 2019
Publication Date Oct 16, 2019
Deposit Date Oct 16, 2019
Publicly Available Date Mar 28, 2024
Journal Sustainability
Electronic ISSN 2071-1050
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 11
Issue 20
Article Number 5718
DOI https://doi.org/10.3390/su11205718
Keywords Mixed-integer non-linear programming; Linearised model; Decarbonisation investment; Environmental protection awareness; Taxation policy
Public URL https://nottingham-repository.worktribe.com/output/2848966
Publisher URL https://www.mdpi.com/2071-1050/11/20/5718/htm

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